Being able to automatically track the real-time interactions between individuals in a group setting, for example, is key to understanding many aspects of community dynamics. For example, in an enterprise, organization performance is related to the structure of information flow between individuals, and such structure can be inferred from who interacts with whom and where such interactions occur at any given time. Therefore, from the information on how and where individuals in various branches of an organization behave and interact, the performance of such individuals (e.g., employees of enterprise) and the overall workplace environment can be improved through the reengineering of organizational dynamics.
Today, the proliferation of wearable devices equipped with high-performance mobile sensors has enabled the tracking of individuals and their various movements throughout the day, and a number of data sets have emerged to track the behavior and interactions of individuals within different types of organizations, with varying spatio-temporal resolutions and with different durations.
For example, certain existing solutions use pre-defined parameters to define human encounters such as the threshold of the duration of conversation, or timestamp differences of infra-red (IR) scanning, or the strength of received signal strength indicators (RSSI). However, given the proximity between individuals is calculated based on such ad-hoc parameters these techniques have certain limitations with respect to their adaptability in reflecting the dynamic environments of human interaction. Further, given this adaptability limitation, location tracking is critical for these approaches thereby increasing the automation challenge. In particular, location estimation based on indoor sensor network data is highly inaccurate due to the data's noisy nature inherent to low-power sensor signals, interference between objects in the same space, mobility factors, and other similar factors. As will be understood, known time-series clustering techniques could be used with respect to the sensor data in order to identify similarity between these time series, which would be a measure for physical proximity and interaction. However, unlike common time series clustering methods, the sensor signals need to be handled differently because of the high-level of noise and dynamics in group interactions.
Therefore, a need exists for an improved community discovery technique that enables the accurate real-time determination of linkages between groups of individuals in a data-driven manner that is insensitive to noise and/or missing/dropped signals for automatically capturing the dynamic real-time interaction between individuals.